Predicting the shear strength of reinforced concrete beams using artificial neural networks

Mansour, Mohamad; Dicleli, Murat; Lee, J.Y.; Zhang, J.

doi:10.1016/j.engstruct.2004.01.011

Cited by 182 publications

(76 citation statements)

References 11 publications

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“…It is felt that this is partly due to the complexity of the phenomenon involved and partly because of the limitations of statistical regression, an analytical tool commonly used by most of the investigators. Neural networks (NN) have advantages over statistical models like their data-driven nature, model-free form of predictions, and tolerance to data errors [11,12,16,18]. The objective of this study is to reanalyze the data considered in earlier studies by employing the NN technique with a view towards finding out if better predictions are possible.…”

Section: Aims and Scope Of The Researchmentioning

confidence: 99%

“…Three neuron models namely, 'tansig', 'logsig' and 'purelin', have been used in the architecture of the network with the back propagation algorithm implemented in originally developed MATLAB routines. In the back propagation algorithm, the feed-forward (FFBP), cascade-forward (CFBP) and Elman back propagation (EBP) type network were considered [3,11,12,16,18,23]. Each input is weighted with an appropriate weight and the sum of the weighted inputs and the bias forms the input to the transfer function.…”

Section: Neural Network Modelmentioning

confidence: 99%

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Estimation of RC slab-column joints effective strength using neural networks

Shah

Ribakov

2011

Lat. Am. j. solids struct.

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The nominal strength of slab-column joints made of highstrength concrete (HSC) columns and normal strength concrete (NSC) slabs is of great importance in structural design and construction of concrete buildings. This topic has been intensively studied during the last decades. Different types of column-slab joints have been investigated experimentally providing a basis for developing design provisions. However, available data does not cover all classes of concretes, reinforcements, and possible loading cases for the proper calculation of joint stresses necessary for design purposes. New numerical methods based on modern software seem to be effective and may allow reliable prediction of column-slab joint strength. The current research is focused on analysis of available experimental data on different slab-to-column joints with the aim of predicting the nominal strength of slabcolumn joint. Neural networks technique is proposed herein using MATLAB routines developed to analyze available experimental data. The obtained results allow prediction of the effective strength of column-slab joints with accuracy and good correlation coefficients when compared to regression based models. The proposed method enables the user to predict the effective design of column-slab joints without the need for conservative safety coefficients generally promoted and used by most construction codes.

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Section: Aims and Scope Of The Researchmentioning

confidence: 99%

Section: Neural Network Modelmentioning

confidence: 99%

Estimation of RC slab-column joints effective strength using neural networks

Shah

Ribakov

2011

Lat. Am. j. solids struct.

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“…ANNs are data processing systems consisting of a large number of simple, highly interconnected processing elements (artificial neurons) in an architecture inspired by structure of the central cortex of the brain (Hola and Schabowicz, 2005;Mansour et al, 2004). Much of success of neural network is due to such characteristic as nonlinear processing and parallel processing.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting the permeability of pervious concrete pavement using artificial neural networks modeling

Ghashghaei¹,

Hassani²

2015

Sci. Res. Essays

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Pervious concrete (PC) pavement is a sustainable type of concrete pavement that can protect and restore natural ecosystem. The permeability coefficient is the most important characteristic of PC. The purpose of this experimental study was to investigate the effect of mixture design parameters, particularly water-to-cement ratio (W/C) and size of aggregate on the permeability coefficient of PC. The thirty six mixtures were made with W/C in range of 0.28 to 0.34, 350 kg/m 3 cement content and 9.5 to 19.5 mm maximum size of aggregate. In this study the feasibility of using the artificial neural networks (ANN) in predicting the effect of aggregate size and W/C on amount of permeability coefficient of PC was investigated. For modeling, 65% of data was used for model training and remaining 35% was used for model testing. Based on the lowest root mean squared error (RMSE), the best ANN model was chosen. The results showed that the W/C and aggregate size are key parameter, which significantly affect the performance of PC. The ANN modeling was developed in this study can facilitate prediction permeability coefficient of PC. This approach can reduce the number of trial batches for target performance of samples.

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“…They have the ability to learn from the experience in order to improve their performance and to adapt themselves to changes in the environment [26,27]. The typical network has 1 input layer, 1 or more hidden layers, and 1 output layer.…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

2017

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Abstract. Cutting tool wear in machining processes reduces the product surface quality, a ects the dimensional and geometrical tolerances, and causes tool breakage during the metal cutting. Therefore, online tool wear monitoring is needed to prevent reduction in machining quality. An Arti cial Neural Network (ANN) model was developed in this study to predict and simulate the tool ank wear. To achieve this aim, an experiment array was provided using full factorial method, and the tests were conducted on a CNC lathe machine tool. Vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool ank wear. For designing the model, the cutting parameters, cutting forces, and vibration amplitude were de ned as model inputs, and tool ank wear was selected as an output. The model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. The estimated and measured results were then compared with each other. Based on the comparison results, maximum squared error values are under 6 10 14 mm, and R 2 is 1, meaning that the designed model can predict the results with high and reliable accuracy.

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Predicting the shear strength of reinforced concrete beams using artificial neural networks

Cited by 182 publications

References 11 publications

Estimation of RC slab-column joints effective strength using neural networks

Estimation of RC slab-column joints effective strength using neural networks

Predicting the permeability of pervious concrete pavement using artificial neural networks modeling

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

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